'Chemistry at the speed of sound': automated 1536-well nanoscale synthesis of 16 scaffolds in parallel

Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients

Reaction optimization and characterization depend on reliable measures of reaction yield, often measured by high-performance liquid chromatography (HPLC). Peak areas in HPLC chromatograms are correlated to analyte concentrations by way of calibration standards, typically pure samples of known concentration. Preparing the pure material required for calibration runs can be tedious for low-yielding reactions and technically challenging at small reaction scales. Herein, we present a method to quantify the yield of reactions by HPLC without needing to isolate the product(s) by combining a machine learning model for molar extinction coefficient estimation, and both UV-vis absorption and mass spectra. We demonstrate the method for a variety of reactions important in medicinal and process chemistry, including amide couplings, palladium catalyzed cross-couplings, nucleophilic aromatic substitutions, aminations, and heterocycle syntheses. The reactions were all performed using an automated synthesis and isolation platform. Calibration-free methods such as the presented approach are necessary for such automated platforms to be able to discover, characterize, and optimize reactions automatically.

Novel SK channel positive modulators prevent ferroptosis and excitotoxicity in neuronal cells

Small conductance calcium-activated potassium (SK) channel activity has been proposed to play a role in the pathology of several neurological diseases. Besides regulating plasma membrane excitability, SK channel activation provides neuroprotection against ferroptotic cell death by reducing mitochondrial Ca2+ uptake and reactive oxygen species (ROS). In this study, we employed a multifaceted approach, integrating structure-based and computational techniques, to strategically design and synthesize an innovative class of potent small-molecule SK2 channel modifiers through highly efficient multicomponent reactions (MCRs). The compounds' neuroprotective activity was compared with the well-studied SK positive modulator, CyPPA. Pharmacological SK channel activation by selected compounds confers neuroprotection against ferroptosis at low nanomolar ranges compared to CyPPA, that mediates protection at micromolar concentrations, as shown by an MTT assay, real-time cell impedance measurements and propidium iodide staining (PI). These novel compounds suppress increased mitochondrial ROS and Ca2+ level induced by ferroptosis inducer RSL3. Moreover, axonal degeneration was rescued by these novel SK channel activators in primary mouse neurons and they attenuated glutamate-induced neuronal excitability, as shown via microelectrode array. Meanwhile, functional afterhyperpolarization of the novel SK2 channel modulators was validated by electrophysiological measurements showing more current change induced by the novel modulators than the reference compound, CyPPA. These data support the notion that SK2 channel activation can represent a therapeutic target for brain diseases in which ferroptosis and excitotoxicity contribute to the pathology.

Crystal Clear: Decoding Isocyanide Intermolecular Interactions through Crystallography

The isocyanide group is the chameleon among the functional groups in organic chemistry. Unlike other multiatom functional groups, where the electrophilic and nucleophilic moieties are typically separated, isocyanides combine both functionalities in the terminal carbon. This unique feature can be rationalized using the frontier orbital concept and has significant implications for its intermolecular interactions and the reactivity of the functional group. In this study, we perform a Cambridge Crystallographic Database-supported analysis of isocyanide intramolecular interactions to investigate the intramolecular interactions of isocyanides in the solid state, excluding isocyanide-metal complexes. We discuss examples of different interaction classes, including the isocyanide as a hydrogen bond acceptor (RNC···HX), halogen bonding (RNC···X), and interactions involving the isocyanide and carbon atoms (RNC···C). The latter interaction serves as an intriguing illustration of a Bürgi-Dunitz trajectory and represents a crucial experimental detail in the well-known multicomponent reactions such as the Ugi- and Passerini-type mechanisms. Understanding the spectrum of intramolecular interactions that isocyanides can undergo holds significant implications in fields such as medicinal chemistry, materials science, and asymmetric catalysis.

Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back

A closed-loop, autonomous molecular discovery platform driven by integrated machine learning tools was developed to accelerate the design of molecules with desired properties. We demonstrated two case studies on dye-like molecules, targeting absorption wavelength, lipophilicity, and photooxidative stability. In the first study, the platform experimentally realized 294 unreported molecules across three automatic iterations of molecular design-make-test-analyze cycles while exploring the structure-function space of four rarely reported scaffolds. In each iteration, the property prediction models that guided exploration learned the structure-property space of diverse scaffold derivatives, which were realized with multistep syntheses and a variety of reactions. The second study exploited property models trained on the explored chemical space and previously reported molecules to discover nine top-performing molecules within a lightly explored structure-property space.

Direct-to-biology, automated, nano-scale synthesis, and phenotypic screening-enabled E3 ligase modulator discovery

Thalidomide and its analogs are molecular glues (MGs) that lead to targeted ubiquitination and degradation of key cancer proteins via the cereblon (CRBN) E3 ligase. Here, we develop a direct-to-biology (D2B) approach for accelerated discovery of MGs. In this platform, automated, high throughput, and nano scale synthesis of hundreds of pomalidomide-based MGs was combined with rapid phenotypic screening, enabling an unprecedented fast identification of potent CRBN-acting MGs. The small molecules were further validated by degradation profiling and anti-cancer activity. This revealed E14 as a potent MG degrader targeting IKZF1/3, GSPT1 and 2 with profound effects on a panel of cancer cells. In a more generalized view, integration of automated, nanoscale synthesis with phenotypic assays has the potential to accelerate MGs discovery.